Algorithmic identification of binding specificity mechanisms in proteins

项目摘要 蛋白质结合偏好的变化是精确治疗疾病的关键障碍。当高 蛋白质的分辨率结构是可用的，并且蛋白质的许多同种型已经连接到不同的分辨率结构。 考虑到结合偏好，原则上有可能模拟所有同种型的结构并发现它们之间的相互作用。 导致绑定偏好变化的机制。不幸的是，这个发现过程取决于 人类的专业知识，检查分子结构，并考虑到数百种亚型可能存在，一个人， 将无法客观地检查许多相似的同种型。为了填补这一空白，该项目将（A1）- velop软件可以识别导致差异结合偏好的结构机制， 相似的结构机制，并解释了英语的机制。本项目的第二个目标（A2）是 为了在大规模的蛋白质家族上验证该软件， 偏好，并通过与实验合作者的盲目预测。 我们的方法包括创建软件，模仿结构化的视觉推理技术， 生物学家在研究分子结构时。这些技术不仅是大多数主要疾病的原因， 结构生物学中的掩护，但它们也很容易通过非计算研究来理解- 呃。此属性将使我们的软件能够立即集成到实验室的现有工作流程中， 专注于计算方法。此属性还与现有方法形成对比，现有方法通常输出 结构模型，势能，p值和结构分数，这些对于非专家来说很难理解， 理解或纳入他们的研究。通常，生物物理学专家需要解释输出， 它们可以在实验室环境中操作。 在初步结果中，我们的方法已经确定了控制特异性的分子机制， 几个蛋白质家族。对同行评审实验的验证证明了初步的 结果在几乎所有情况下都正确。我们的方法也已被应用于盲预测的结合 蓖麻毒素中的机制，它结合并破坏人类核糖体。与实验合作- rators，我们表明，我们的方法正确地识别和预测了几个氨基酸的作用， 迄今未知的识别核糖体的作用。利用我们的方法论和严格的验证- 该项目将产生一个高度有效的，可用的软件包，将弥合关键差距 精确治疗和诊断的发展。

Project Summary Variations in protein binding preferences are a critical barrier to the precision treatment of disease. When high resolution structures of a protein are available, and many isoforms of the protein have been connected to dif- fering binding preferences, it is possible in principle to model the structures of all isoforms and discover the mechanisms that cause variations in binding preferences. Unfortunately, this discovery process depends on human expertise for examining molecular structure, and given that hundreds of isoforms may exist, a human would be overwhelmed to objectively examine many similar isoforms. To fill this gap, this project will (A1) de- velop software that identifies structural mechanisms that cause differential binding preferences, categorizes similar structural mechanisms, and explains the mechanisms in English. The second aim of this project (A2) is to validate the software at a large scale on families of proteins that exhibit a variety of well-examined binding preferences, and through blind predictions with experimental collaborators. Our approach involves creating software that mimics the visual reasoning techniques employed by structural biologists when examining molecular structures. Not only are these techniques responsible for most major dis- coveries in structural biology, but they are also straightforward to understand by non-computational research- ers. This property will enable our software to immediately integrate into existing workflows at labs that do not focus on computational methods. This property also contrasts from existing methods, which generally output structural models, potential energies, p-values and structural scores which are difficult for non-experts to un- derstand or incorporate into their research. Often, an expert in biophysics is required to interpret the outputs so that they can be operationalized in laboratory environments. In preliminary results, our methods have already identified molecular mechanisms that govern specificity in several families of proteins. Verification against peer-reviewed experimentation has proven the preliminary results correct in almost all cases. Our methods have also been applied to make a blind prediction of binding mechanisms in the ricin toxin, which binds to and damages the human ribosome. With experimental collabo- rators, we showed that our methods correctly identified and predicted the roles of several amino acids with a hitherto unknown role in recognizing the ribosome. Using our methodological approach and our rigorous valida- tion strategy, this project will produce a highly validated, usable software package that will bridge a critical gap in the development of precision therapies and diagnostics.